Computational modeling of drug disposition

Computational modeling of drug disposition

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  CONTENTS • Introduction • Modelingtechniques • Drug absorption i. solubility ii. intestinal permeation iii. other considerations • Drug distribution • Drug excretion 2
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  • Active Transport i.P-gp ii. BCRP iii. Nucleoside transporters iv. hPEPT1 v. ASBT vi. OCT vii. OATP viii. BBB-choline transporter • References 3
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  INTRODUCTION • Historically ,drug discovery has focused almost exclusively on efficacy and selectivity against the biological target. • As a result, nearly half of drug candidates fail at phase 2 and phase 3 clinical trials because of undesirable drug pharmacokinetics properties , including absorption , distribution, metabolism, excretion , and toxicity (ADMET). • The pressure to control the escalating cost of new drug development has changed the paradigm since the mid- 1990s. • To reduce the attrition rate at more expensive later stages , in vitro evaluation od ADMET properties in the early phase of drug discovery has been widely adopted. 4
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  • Many high-throughputand in vitro ADMET property screening assays have been developed and applied successfully. • For example: Caco-2 and MDCK ( Madin - Darby canine kidney Epithelial Cells ) monolayers are widely used to stimulate membrane permeability as an in vitro estimation of in vivo absorption. • These in vitro results have enabled the training of in silico models , which could be applied to predict the ADMET properties of compounds even before they are synthesized. • Fueled by the ever-increasing computational power and significant advances of in silico modeling algorithms , numerous computational programs that aim at modeling drug ADMET properties have emerged. 5
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  • A comprehensivelist of available commercial ADMET modeling software has been provided previously by van de Waterbeemd and Gifford. • In these chapter focuses on in silico modeling of drug disposition including absorption , distribution , and excretion. • This chapter concludes with the challenges and future trends of in silico drug disposition property modeling. 6
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  MODELING TECHNIQUES 8
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  MODELING TECHNIQUES • Twotypes of modeling approaches are: 1. Quantitative approaches 2. Qualitative approaches. 9
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  1. QUANTITATIVE APPROACHES •The quantitative approaches represented by pharmacophore modeling and flexible docking studies investigate the structural requirements for the interaction , between drugs and the targets that are involved in ADMET processes. • These are especially useful when there is an accumulation of knowledge against a certain target. • For example , a set of drugs known to be transported by a transporter would enable a pharmacophore study to elucidate the minimum required structural features for transport. • The availability of a protein’s three-dimensional structure , from either X-ray crystallization or homology modeling , would assist flexible docking of the active ligand to derive important interactions between the protein and the ligand. 10
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  • Three widelyused automated pharmacophore perception tools. a) DISCO (DIStance Comparisons) b) GASP (Genetic algorithm similarity program) c) catalyst/HIPHOP • All three programs attempt to determine common features based on the superposition of active compounds with different algorithms. • The application of different flexible docking algorithms in drug discovery has recently been reviewed. • The essential interactions derived from either study can be used as a screen in evaluating drug ADMET properties. 11
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  2.QUALITATIVE APPROACHES • Itrepresented by quantitative structure-activity relationship (QSAR ) and quantitative structure-property relationship (QSPR) Studies utilize multivariate analysis to correlate molecular descriptors with ADMET- related properties. • A diverse range of molecular descriptors can be calculated based on the drug structure. • Some of these descriptors are closely related to a physical property and are easy to comprehend (e.g., molecular weight ),whereas the majority of the descriptors are of quantum mechanical concepts or interaction energies at dispersed space points that are beyond simple physicochemical parameters. 12
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  • When calculatingcorrelations , it is important to select the molecular descriptors that represent the type of interactions contributed to the targeted biological property. • A set of descriptors that specifically target ADME related properties has been proposed by Cruciani and colleagues. • The majority of published ADMET models are generated based on 2D descriptors. • Even though the alignment-dependent 3D descriptors that are relevant to the targeted biological activity tend to generate the most predictive models. 13
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  • The difficultiesinherent in structure alignment thwart attempts to apply this type of modeling in a high- throughput manner. This has prompted the development of alignment independent 3D descriptors. However, most of these descriptors to date are still insufficiently discriminating. • A wide selection of statistical algorithms is available to researchers for correlating field descriptors with ADMET properties including 1. Simple multiple linear regression (MLR) 2. Multivariate partial least-squares (PLS) 14
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  • Nonlinear regression-type i.Algorithms Artificial neural networks (ANN) ii. Support vector machine (SVM) • No one method can consistently perform better than the others. Just like descriptor selection, it is essential to select the right mathematical tool for most effective ADMET modeling. Sometimes it is necessary to apply multiple statistical methods and compare the results to identify the best approach, as illustrated in a recent solubility QSPR model 15
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  DRUG ABSORPTION 17
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  DRUG ABSORPTION • Becauseof its convenience and good patient compliance , oral administration is the most preferred drug delivery form. • As a result , much of the attention of in silicon approaches is focused on modeling drug oral absorption , which mainly occurs in the human intestine. • In general drug bioavailability and absorption is the result of the interplay between drug solubility and intestinal permeability. 18
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  SOLUBILITY • A druggenerally must dissolve before it can be absorbed from the intestinal lumen. Direct measurement of solubility is time-consuming and requires a large amount of (expensive) compound at the milligram scale • By measuring a drug’s log P value (log of the partition coefficient of the compound between water and n- octanol)and its melting point , one could indirectly estimate solubility using the “general solubility equation”. • Even though the process is simplified, it still requires the synthesis of the compound. • To predict the solubility of the compound even before synthesizing it , in silico modeling can be implemented. 19
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  There are mainlytwo approaches to modeling solubility. 1. One is Based on the underlying physiological processes. 2. Other is an empirical approach. • The dissolution process involves the breaking up of the solute from its crystal lattice and the association of the solute with solvent molecules. Obviously, weaker interactions within the crystal lattice (lower melting point) and stronger interactions between solute and solvent molecules will result in better solubility and vice versa. For drug like molecules, solvent-solute interaction has been the major determinant of solubility and its prediction attracts most efforts. • Log P is the simplest estimation of solvent-solute interaction and can be readily predicted with commercial programs such as CLogP ( Daylight Chemical Information systems , aliso Viejo ,CA) which utilizes a fragment based approach. 20
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  • To recognizethe contribution of solute crystal lattice energy in determining solubility, other approaches amended LogP values with additional terms for more accurate predictions. • Empirical approaches , represented by QSPR , utilize multivariate analyses to identify correlations between molecular descriptors and solubility. • Even though the calculation process ignores the underlying physiological processes, the molecular descriptor selection and model interpretation still requires understanding of the dissolution process. • Selection of field descriptors that adequately describe the physiological process and the appropriate multivariate analysis is essential successful modeling. 21
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  INTESTINAL PERMEATION • Intestinalpermeation describes the ability of drugs to cross the intestinal mucosa separating the gut lumen from the portal circulation. • It is an essential process for drugs to pass the intestinal membrane before entering the systemic circulation to reach their target site of action. • The process involves both passive diffusion and active transport. • It is a complex process that is difficult to predict solely based on molecular mechanism. 22
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  • As aresult , most current models aim to simulate in vitro membrane permeation of Caco-2 , MDCK or PAMPA , which have been a useful indicator of in vivo drug absorption. 23
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  OTHER CONSIDERATIONS • Theionization state will affect both solubility and permeability and , as a result , influence the absorption profile of a compound. • Given the environmental pH , the charge of a molecule can be determined using the compound’s ionization constant value (pka) , which indicates the strength of an acid or a base. • Several commercially and publicly available programs provide pka estimation based on the input structure , including • SCSpka ( Chemsilico , Tewksbury , MA ) • Pallas/pKalc ( CompuDrug , Sedona , AZ) • ACD / pKa (ACD ,Toronto , ON , Canada ) • SPARC Online calculator. 24
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  • Both influxand efflux transporters are located in intestinal epithelial cells and can either increase or decrease oral absorption. • Influx transporters such as human peptide transporter 1 (hPEPT1), apical sodium bile acid transporter (ASBT ) , and nucleoside transporters actively transport drugs that mimic their native substrates across the epithelial cell. • Efflux transporters such as P-glycoprotein (P-gp) , multidrug resistance-associated protein (MRP) , and Breast Cancer resistance protein (BCRP) Actively pump absorbed drugs back into the intestinal lumen. • Commercial packages such as Gastro plus ( simulations plus , Lancaster , CA ) and iDEA ( Lion Bioscience , Inc. Cambridge , MA) are available to predict oral absorption and other pharmacokinetic properties. • They are both based on the advanced compartmental absorption and transit (CAT) model [20], which incorporates the effects of drug moving through the gastrointestinal tract and its absorption into each compartment at the same time 25
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  DRUG DISTRIBUTION 26
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  DRUG DISTRIBUTION • Distributionis an important aspect of a drug’s pharmacokinetic profile. • The structural and physiochemical properties of a drug determine the extent of its distribution , which is mainly reflected by three parameters: 1. Volume of distribution (VD) 2. Plasma- protein binding ( PPB) 3. Blood-brain barrier (BBB) Permeability 27
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  1.VOLUME OF DISTRIBUTION(VD) • Vd is a measure of relative partitioning of drug between plasma and tissue, an important proportional constant that, when combined a drug is a major determinant of how often the drug should be administered. • However, because of the scarcity of in vivo data and complexity of the underlying processes, computational models that are capable of prediction Vd based solely on computed descriptors are still under development. 28
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  2.PLASMA PROTEIN BINDING(PBP) • Drugs binding to a variety of plasma proteins such as serum albumin, as unbound drug primarily contributes to pharmacological efficacy. • The effect of PPB is an important consideration when evaluating the effective (unbound) drug plasma concentration. • The models proposed to predict PBB should not rely on the binding data of only one protein when predicting plasma protein binding because it is a composite parameter reflecting interactions with multiple protein. 29
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  3.BLOOD-BRAIN BARRIER (BBB) •The BBB maintains the restricted extracellular environment in the central nerve system. • The evaluation of drug penetration through the BBB is an integral part of drug discovery and development process. • Again, because of the few experimental data derived from inconsistent protocols, most BBB permeation prediction models are of limited practical use despite intensive efforts. • Most approaches model log blood/brain (logBB), which is a measurement of the drug partitioning between blood and brain tissue. • The measurement is an indirect implication of BBB permeability, which does not discriminate between free and plasma protein-bound solute. 30
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  DRUG EXCRETION 31
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  DRUG EXCRETION • Theexcretion or clearance of a drug is quantified by plasma clearance, which is defined as plasma volume that has been cleared completely free of drug per unit of time. • Together with Vd, it can assist in the calculation of drug half-life, thus determining the dosage regimen. • Hepatic and renal clearances are the two main components of plasma clearance. • No model has been reported that is capable of predicting plasma clearance solely from computed drug structures. • Current modeling efforts are mainly focused on estimating in vivo clearance from in vitro data. • Just like other pharmacokinetic aspects, the hepatic and renal clearance process is also complicated by presence of active transporters. 32
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  ACTIVE TRANSPORTERS 33
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  ACTIVE TRANSPORTERS • Transportersshould be an integral part of any ADMET modeling program because of their ubiquitous presence on barrier membranes the substantial overlap between their substance many drugs. • Unfortunately, because of our limited understanding of transporters, most prediction programs do not have mechanism to incorporate the effect of active transport. • However, interest in these transporters has resulted in a relatively large amount of in vitro data, which in turn have enabled the generation of pharmacophore and QSAR models for many of them. 34
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  • These modelshave assisted in the understanding of the complex effects of transporters on drug disposition, including absorption, distribution and excretion. • Their incorporation into current modeling programs would also result in more accurate prediction of drug disposition behavior. 35
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  P-GLYCOPROTEIN TRANSPORTER (P-gp) •P- glycoprotein is an ATP dependent efflux transporter that transports a broad range of substrates out of the cell. • It affects drug disposition by reducing absorption and enhancing renal and hepatic excretion. • For example , P-gp is known to limit the intestinal absorption of the anticancer drug paclitaxel and restricts the CNS penetration of HIV protease inhibitors. • It is also responsible for multiple drug resistance in cancer chemotherapy. • Because of its significance in drug disposition and effective cancer Treatment, • P-gp attracted numerous efforts and has become the most extensively studied transporter, with abundant experimental data. 36
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  • Ekins andcolleagues generated five computational pharmacophore models to predict the inhibition of P-gp from in vitro on a diverse set of inhibitors with several cell system , including inhibition of digoxin transport and verapamil binding in Caco-2 cells; vinblastine and calcein accumulation in P-gp-expressing LLC-PK1 (L-MDR1) cells; and vinblastine binding in vesicles derived from CEM/VLB100 cells • By comparing and merging all P-gp pharmacophore models, common areas of identical chemical features such as hydrophobes , hydrogen bond acceptor , and ring aromatic features as well as their geometric arrangement were identified to be the substrate requirement for P-gp. 37
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  • Similar transportrequirements were reiterated in other works . • More recently Cianchetta and colleagues combined alignment-independent 3D descriptors and physicochemical descriptors to model inhibition of calcein accumulation in Caco-2 cells . • Using a diverse set of 129 compounds, the authors derived a robust QSAR model that revealed two hydrophobic features, two hydrogen bond acceptors, and the molecular dimension to be essential determinants of P-gp-mediated transport. • These identified transport requirements not only to help screen compounds with potential reflux related bioavailability problems, but also to assist the identification of P-gp inhibitors. 38
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  • which whencoadministered with target drugs would optimize their pharmacokinetic profile by increasing bioavailability. • In fact, a recent pharmacophore-based database screening has proposed 28 novel P-gp inhibitors from the Derwent World Drug Index . • Our own Catalyst pharmacophore searches of databases have also guided the identifi - cation of several currently prescribed drugs that are P-gp inhibitors (μM), which was previously unknown 39
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  •inhibition of P-gp •The inhibition of efflux pump is mainly done in order to improve the delivery of therapeutic agents. In general, P-gp can be inhibited by three mechanisms: (i) blocking drug binding site either competitively, non-competitively or allosterically; • (ii) interfering with ATP hydrolysis; and (iii) altering integrity of cell membrane lipids.1,10,17–19 • The goal is to achieve improved drug bioavailability, uptake of drug in the targeted organ, and more efficacious cancer chemotherapy through the ability to selectively block the action of P-gp. Inhibitors are as structurally diverse as substrates.19 Many inhibitors (verapamil, cyclosporin A, transflupenthixol, etc.) are themselves transported by P-gp. 40
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  BREAST CANCER RESISTANCEPROTEIN (BCRP) • Breast cancer resistance protein is another ATP dependent efflux transporter that confers resistance to a variety of anticancer agents anthracyclines. • In addition to high level of expression in hematological malignancies and solid tumors, BCRP is also expressed in intestine, liver and brain thus implicating its very complicated role in drug disposition behavior. • Zhang and colleagues generated a BCRP 3DQSAR model by analyzing structure and activity of 25 flavonoid analogs 41
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  • The modelanaphasizes very specific structural feature requirements for BCRP such as the presence of a 2,3-double bond in ring C and hydroxylation at position 5. • Because the model in only based on a set of closely related structure instead of diverse set, it should be applied with caution. • Satisfying the transport model would render a compound susceptible to BCRP, but not fitting into the model does not necessarily exclude the candidate from BCRP transport. • In fact, this caveat should be considered for all predictive in silico models, because no model can cover all possible chemical space. 42
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  Figure 20.2 Pharmacophoremodels for P-gp inhibition. A. P-gp inhibition pharmacophore aligned with the potent inhibitor LY335979. B. P-gp substrate pharmacophore aligned with verapamil. C. P-gp inhibition pharmacophore 2 aligned with LY335979. Green indicates H-bond acceptor feature, and cyan indicates hydrophobic feature. See color plate. 43
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  NUCLEOSIDE TRANSPORTER • Nucleosidetransporters transport both naturally occurring nucleoside and synthetic nucleoside analogs that are used as anticancer drugs anti viral drugs. • There are various types of nucleoside transporter, including concentrative nucleoside transporter (CNT1 CNT2 CNT3) and equilibrative nucleoside transporter(ENT1 ENT2 ENT3) each have different substrate specificity. • ENT have broad affinity, low selectivity and are ubiquitously located. • CNT have high affinity, selective located in epithelia of intestine kidney, liver and brain, indicating their involvement in drug disposition, distribution and excretion. 44
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  • The first3D-QSAR model for nucleoside transporter was generated back in 1190. • It is an oversimplified general model limited by the scarce experimental data at that time. A • more comprehensive study generated distinctive models for CNT1, CNT2, and ENT1 with both pharmacophore and 3DQSAR modeling techniques • All models show the common features required for nucleoside transporter mediated transport: two hydrophobic features and one hydrogen bond acceptor on the pentose ring. 45
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  • The individualmodels also reveal the subtle characteristic requirements for each specific transporter. • The modeling results also support the previous observation that CNT2 is the most selective transporter whereas ENT1 has the broadest inhibitor specificity. • More recently, we performed the same analyses and generated pharmacophore and 3D-QSAR models for CNT3 by assessing the transport activity of 33 nucleoside analogs . • These studies represent a comprehensive evaluation of transport requirements of all three types of CNTs. 46
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  • Human peptidetransporter is a low affinity high capacity to peptide transport system that transport a diverse range of substrate including B-lactam antibiotics and ACE inhibitors. • It is mainly expressed in intestine and kidney affecting drug absorption and excretion. • A pharmacophore model is based on three high affinity substrates(gly-sar, bestatin, enalapril) were taken • They recognize two hydrophobic features, one hydrogen bond donor, one hydrogen bond acceptor, and negative ionizable feature to be hPEPT1 transport requirements. 47 HUMAN PEPTIDE TRANSPORTER (hPEPT1)
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  • The pharmacophoremodel was subsequently applied to screen the CMC database with over 8000 drug like molecules. • The anti Diabetic repaglinide and HMG- CoA reductase inhibitor Fluvastatin were suggested by the model and later verified to inhibit Hpept1 with submillimolar potency. • This work demonstrated the potential of applying in silico models in high throughput database screening. 48
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  HUMAN APICAL SODIUM-DEPENDENTBILE ACID TRANSPORTER (ASBT) • The human apical sodium- dependent bile acid transporter is high efficacy, high capacity transporter expressed on the apical membrane of intestinal epithelial cells and cholangiocytes. • It assist absorption of bile acid and their analogs, thus providing a additional intestinal target for improving drug absorption. • Baringhaus and colleagues developed a pharmacophore model based on a training set of 17 chemically diverse inhibitors of ASBT. 49
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  • The modelrevealed ASBT transport requirements as one hydrogen bond donor, one hydrogen bond acceptor, one negative charge, and three hydrophobic centres. • These 3D- QSAR model derived from the structure and activity of 30 ASBT inhibitors and substrate. 50
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  ORGANIC CATIONIC TRANSPORTER(OCT) • The organic cation transporter facilitate the uptake of many cationic drugs across different membranes of kidney and intestine epithelia. • A broad range of drugs or their metabolites fall into chemical class of organic cation including antiarrythmics , B-adrenoaceptor blocking agents, Antihistaminics , antiviral agents , and skeletal muscle- relaxing agents • These OCTs have been cloned from different species, OCT1/2/3. • A human OCT pharmacophore model was developed by analyzing the extent of inhibition of TEA uptake in HeLa cells of 22 diverse molecules. 51
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  • The modelsuggests the transport requirements of human OCT1 as three hydrophobic features and one positive ionizable feature . Molecular determinants of substrate binding to human OCT2 and rabbit OCT2 were recently reported • Both 2D and 3D-QSAR analyses were performed to identify and discriminate the binding requirements of two orthology. • The models showed the same chemical features, highlighting their similarities. However, the orientation of a critical hydrogen bonding feature set the two orthologs apart. • This work illustrates the sensitivity of in silico modeling in discriminating similar transporters. 52
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  ORGANIC ANION TRANSPORTING POLYPEPTIDE(OATP) • Organic anion transporting polypeptides influence the plasma conc. of many drugs by actively transporting them across various tissue membranes such as liver, intestine, lung and brain. • Because of their broad substrate specificity,OATP transport not only organic anionic drugs but also organic cationic drugs. • human OATPs have been identified, and the substrate binding requirements of the best-studied OATP1B1 were successfully modeled with metapharmacophre approach recently. • Through assessing a training set of 18 diverse molecules, the metapharmacophore model identifies three hydrophobic features flanked by two hydrogen bond acceptor features to be essential requirement for OATP1B1 transport. 53
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  BBB-CHOLINE TRANSPORTER • TheBBB-choline transporter is a native nutrient transporter that transports choline, a charged cation, across the BBB into the CNS. • Its active transport assists the BBB penetration of choline like compounds, and understanding its structural requirements should afford a more accurate prediction of BBB permeation. Even though the BBB-choline transporter has not been cloned, • Geldenhuys and colleagues applied a combination of empirical and theoretical methodologies to study its binding requirements. 55
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  • The 3D-QSArmodels were built with emperical ki data obtained from in situ rat brain perfusion experiments with structurally diverse set of compounds were identified to be important for BBB-choline transporter recognition. • Even though the model statistical significance is not optimal (q2 < 0.5), it does provide a useful estimation of BBB-choline transporter binding requirements. • More accurate in silico models could be generated once higher-quality data from the cloned BBB-choline transporter are available. 56
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  REFERENCES • Computer Applicationsin Pharmaceutical Research and Development , Sean Ekins , 2006 , john wiley & sons. 58
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